CN117563184B - Energy storage fire control system based on thing networking - Google Patents
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Abstract
The invention discloses an energy storage fire control system based on the Internet of things, and particularly relates to the technical field of the Internet of things, comprising a data acquisition module, a data preprocessing module, a data analysis module, a system monitoring module, a system control module and a system execution module; the data analysis module is used for constructing a neural network model to analyze the data, so that the abnormal type and development trend of the energy storage PACK can be accurately judged; the abnormal degree coefficient of the energy storage PACK is calculated through the system monitoring module, the abnormal alarm grade is divided according to the abnormal degree coefficient, the hierarchical alarm is realized, and the response speed and the accuracy of the system are improved; implementing a corresponding control strategy according to the primary alarm information or the secondary alarm information through a system control module; the system executing module executes the instruction sent by the system control module and feeds back the execution result and the state information to the system control module, so that closed-loop control and management of the system are realized, and the reliability and maintainability of the system are improved.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an energy storage fire control system based on the Internet of things.
Background
With the development of renewable energy sources, the popularization of electric vehicles and the increase of industrial and commercial power consumption, the demand of energy storage devices is increasing.
The energy storage equipment possibly breaks down in the operation process, so that fire accidents occur, in order to ensure the safe operation of the energy storage equipment, the energy storage equipment needs to be effectively fire-protected, and therefore the operation of the energy storage equipment is monitored by the existing fire protection system through the sensor, thereby timely treating the fire accidents, preventing the fire accidents from spreading, and causing serious property loss and casualties.
Then, the existing fire-fighting system mainly aims at the whole energy storage cabin or a structure thereof to carry out space-level fire-fighting, the PACK level fire-fighting protection of the energy storage equipment is not perfect, potential abnormal risks cannot be predicted in time, comprehensive evaluation and prediction capability of the health degree of the battery PACK are lacking, the abnormal type and the development trend of the battery PACK cannot be accurately judged, and intelligent decision support is lacking to process the abnormality, so that a fire-fighting control system capable of monitoring and early warning the abnormal condition of the energy storage PACK in real time is needed.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an energy storage fire control system based on the Internet of things, which is used for acquiring original data of an energy storage PACK through a data acquisition module; preprocessing the collected original data through a data preprocessing module; through the data analysis module, a neural network model is constructed to analyze the preprocessed data, so that the abnormal type and development trend of the energy storage PACK can be accurately judged, and comprehensive health evaluation and prediction capability is provided for the system; calculating an abnormal degree coefficient of the energy storage PACK according to the health degree index, the abnormal type data and the development trend predicted value of the abnormal type data by a system monitoring module, and dividing an abnormal alarm grade according to the abnormal degree coefficient to realize hierarchical alarm and improve the response speed and accuracy of the system; implementing a corresponding control strategy according to the primary alarm information or the secondary alarm information through a system control module; the system executing module executes the instruction sent by the system control module and feeds back the execution result and the state information to the system control module, so that closed-loop control and management of the system are realized, and the reliability and maintainability of the system are improved, so that the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: an energy storage fire control system based on thing networking includes:
and a data acquisition module: the data preprocessing module is used for acquiring the original data of the energy storage PACK and transmitting the original data to the data preprocessing module for processing and analysis; the raw data comprises battery pack temperature, battery pack humidity, gas concentration in a battery box, battery pack electric quantity, battery pack charging current, battery pack discharging voltage, battery pack vibration and battery box pressure data;
and a data preprocessing module: the method comprises the steps of preprocessing collected original data, including noise removal, filtering and calibration operations;
and a data analysis module: the method comprises the steps of analyzing preprocessed data, identifying abnormal type data to judge the abnormal type by constructing a neural network model, and predicting the development trend of the abnormal type data;
the system monitoring module comprises a parameter calculation unit and a fault alarm unit; the parameter calculation unit is used for calculating the health index of the energy storage PACK according to the preprocessed data of the energy storage PACK; the fault alarm unit is used for calculating an abnormal degree coefficient of the energy storage PACK according to the health degree index, the abnormal type data and the development trend predicted value of the abnormal type data of the energy storage PACK, dividing the abnormal alarm grade according to the abnormal degree coefficient and transmitting the abnormal alarm grade to the system control module;
and a system control module: the system monitoring module is used for receiving data transmitted by the system monitoring module and implementing a corresponding control strategy according to the primary alarm information or the secondary alarm information;
the system execution module: the system control module is used for executing the instruction sent by the system control module and feeding back the execution result and the state information to the system control module.
In a preferred embodiment, the specific analysis process of the data analysis module is as follows:
a1, marking and classifying the preprocessed data according to different anomaly types to obtain anomaly type data; the abnormality type data includes temperature abnormality data, humidity abnormality data, gas concentration abnormality data, battery state abnormality data, and vibration abnormality data;
a2, extracting temperature characteristics, humidity characteristics, gas concentration characteristics, battery state characteristics and vibration characteristics from the abnormal type data; the temperature characteristics include average temperature, rate of temperature change, and temperature gradient; the humidity characteristics include average humidity and humidity rate of change; the gas concentration characteristics include average oxygen concentration, average smoke concentration, and average harmful gas concentration; the battery state characteristics include battery capacity, battery internal resistance and voltage variation; the vibration characteristics include vibration amplitude and vibration frequency;
a3, building a neural network model through a neural network according to a data set containing various abnormal type samples and the extracted characteristics;
a4, evaluating the performance of the model by using a test set, wherein the performance comprises accuracy, precision and recall index; according to the evaluation result, the network architecture and the super parameters are adjusted to optimize the performance of the model;
a5, deploying the model which is subjected to training and tuning into practical application, and processing unknown data and identifying abnormal type data so as to judge the abnormal type;
a6, predicting the future development trend of the abnormal type data by adopting an autoregressive moving average model.
In a preferred embodiment, the neural network model is built by a neural network based on the data set containing various anomaly type samples and the extracted features, and the processing is as follows:
a31, dividing a data set containing various abnormal type samples into a training set and a testing set;
a32, designing a structure of the neural network, and determining that the number of nodes of an input layer of the neural network is 13 according to the extracted characteristics, wherein each node corresponds to one characteristic; determining the number of hidden layers in the neural network and the number of neurons of each hidden layer; for each neuron, selecting a Sigmoid activation function; training a neural network model by selecting a random gradient descent algorithm;
a33, training the neural network model by using a training set, wherein the processing procedure is as follows:
a331, initializing weight and bias according to the number of nodes of each layer in the network structure, randomly sampling the initialized weight from standard normal distribution, and initializing the bias to zero;
a332, inputting the characteristic data of the training set into a network, calculating the activation value of each neuron through the parameter of each layer, and executing the forward propagation process between the hidden layer and the output layer;
a333, after forward propagation, comparing the predicted result ypr of the model with the actual tag ytr, calculating a Loss function Loss,wherein a represents the number of samples;
a334, using a back propagation algorithm to propagate the error signal from the output layer to the hidden layer, and calculating the gradient of each parameter by using a chain rule;
a335, updating the weight and the bias among layers according to the gradient obtained by calculation by adopting a random gradient descent algorithm;
a336, repeatedly executing the steps A332 to A335 until the preset iteration times are reached or the convergence condition is met.
In a preferred embodiment, the autoregressive moving average model is used for predicting the future development trend of the anomaly type data, and the processing procedure is as follows:
a61, arranging the abnormal type data according to a time sequence, and selecting each minute as a time interval to serve as a time unit of an autoregressive moving average model;
a62, determining the orders g and h of an autoregressive moving average model by observing an autocorrelation graph and a partial autocorrelation graph of the time sequence; wherein the order g represents the number of autoregressive terms and the order h represents the number of moving average terms;
a63, fitting an autoregressive moving average model on historical data by using the selected g and h values;
a64, diagnosing the fitted autoregressive moving average model, and checking whether the residual error accords with a white noise assumption or not;
a65, predicting the future abnormal type data by using the trained autoregressive moving average model; and generating a future predicted value in a recursion mode according to the historical observed value and the model parameter, and obtaining an abnormal type data development trend predicted value.
In a preferred embodiment, the specific processing procedure of the parameter calculation unit is:
b1, calculating a charge/discharge ratio, energy efficiency, cycle efficiency, a charge time recovery coefficient and a self-discharge rate according to the data after the energy storage PACK pretreatment;
b2, calculating the health index ZJK of the energy storage PACK according to the charge/discharge ratio Bcf, the energy efficiency Xnl, the cycle efficiency Xxh, the charge time recovery coefficient Xcs and the self-discharge rate Lzfd,
whereintaAndtbrespectively representing a start time and an end time defining a time range of the integration,α、β、γ、δ、εthe scale factor of each term is represented,kindicating the adjustment factor of the self-discharge rate.
In a preferred embodiment, the specific processing procedure of the fault alarm unit is as follows:
c1, carrying out normalization processing according to a health index ZJK of the energy storage PACK, and converting the health index ZJK into a range from 0 to 1; the specific calculation formula of normalization is as follows:wherein ZJKmin represents the minimum value of the health index ZJK, ZJKmax represents the maximum value of the health index ZJK;
c2, giving weights of different grades according to the quantity and the severity of the abnormal type data, and marking the weight of the abnormal degree as ui;
c3, calculating the abnormality degree coefficient U of the energy storage PACK according to the health degree index GZJK, the abnormality type data and the development trend predicted value of the abnormality type data of the energy storage PACK,wherein D represents the number of abnormal type data, each having a corresponding weight ui and a development trend predictive value Gi;
and C4, judging and comparing the abnormality degree coefficient U of the energy storage PACK with a preset abnormality degree threshold U, if the U is larger than the U threshold, marking the current abnormality type data as first-level alarm information and transmitting the first-level alarm information to the system control module, and if the U is smaller than or equal to the U threshold, marking the current abnormality type data as second-level alarm information and transmitting the second-level alarm information to the system control module.
In a preferred embodiment, the specific processing manner of the system control module is as follows: if the alarm information is the primary alarm information, starting the fire extinguishing device according to the abnormal type and preset fire extinguishing device control logic to extinguish potential fire or thermal events; an alarm signal is sent to personnel in a sound and light flashing mode to remind the energy storage PACK of serious abnormality; isolating the energy storage PACK from the system, and cutting off the connection with other equipment; if the alarm information is the secondary information, adjusting the operation parameters of the energy storage PACK according to the abnormal type data and the development trend predicted value of the abnormal type data; arranging maintenance personnel to overhaul, maintain or replace related parts; and a prompt message is sent to related personnel through a display screen and a mobile phone application mode to remind the energy storage PACK of abnormality, and attention and processing are needed.
The invention has the technical effects and advantages that:
the invention collects the original data of the energy storage PACK through a data collection module; preprocessing the collected original data through a data preprocessing module; the neural network model is constructed through the data analysis module to analyze the preprocessed data, so that the abnormal type and development trend of the energy storage PACK can be accurately judged, comprehensive health evaluation and prediction capability is provided for the system, the fire supervision and management level is improved, and data support is provided for fire supervision management and fire rescue; calculating an abnormal degree coefficient of the energy storage PACK according to the health degree index, the abnormal type data and the development trend predicted value of the abnormal type data by a system monitoring module, and dividing an abnormal alarm grade according to the abnormal degree coefficient to realize hierarchical alarm and improve the response speed and accuracy of the system; implementing a corresponding control strategy according to the primary alarm information or the secondary alarm information through a system control module; the system has the remarkable advantages that wisdom and fire control are organically integrated, the intellectualization of fire-fighting rescue and fire prevention and control is realized, and a new solution is provided for improving fire safety in application scenes such as large-scale and distributed energy storage power stations, mobile energy storage vehicles, standby power energy storage stations and the like.
Drawings
Fig. 1 is a block diagram showing the overall structure of the present invention.
Fig. 2 is a block diagram of a system monitoring module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an energy storage fire control system based on the Internet of things, which is shown in fig. 1-2, and comprises a data acquisition module, a data preprocessing module, a data analysis module, a system monitoring module, a system control module and a system execution module;
the data acquisition module is used for acquiring original data of the energy storage PACK and transmitting the original data to the data preprocessing module for processing and analysis; the raw data comprises battery pack temperature, battery pack humidity, gas concentration in a battery box, battery pack electric quantity, battery pack charging current, battery pack discharging voltage, battery pack vibration and battery box pressure data;
the implementation needs to specifically explain that the specific acquisition mode of the data acquisition module is as follows: the method comprises the steps of arranging sensors around a battery PACK and in a battery box, collecting original data of an energy storage PACK, digitizing and encoding the collected data, and transmitting the data to a data preprocessing module in a wireless communication mode;
the data preprocessing module is used for preprocessing the acquired original data, and comprises noise removal, filtering and calibration operations so as to improve the accuracy and reliability of subsequent data analysis; the noise removal, filtering and calibration operations belong to the prior art means, so this embodiment is not specifically described;
the data analysis module is used for analyzing the preprocessed data, identifying the abnormal type data to judge the abnormal type by constructing a neural network model, predicting the development trend of the abnormal type data, and helping the system to make more accurate judgment and decision so as to start the fire extinguishing device in time;
the implementation needs to specifically explain that the specific analysis process of the data analysis module is as follows:
a1, marking and classifying the preprocessed data according to different anomaly types to obtain anomaly type data; the abnormality type data includes temperature abnormality data, humidity abnormality data, gas concentration abnormality data, battery state abnormality data, and vibration abnormality data; for example, the temperature abnormality data, the humidity abnormality data, and the gas concentration abnormality data are respectively labeled as "temperature abnormality data", "humidity abnormality data", and "gas concentration abnormality data";
a2, extracting temperature characteristics, humidity characteristics, gas concentration characteristics, battery state characteristics and vibration characteristics from the abnormal type data; the temperature characteristics include average temperature, rate of temperature change, and temperature gradient; the humidity characteristics include average humidity and humidity rate of change; the gas concentration characteristics include average oxygen concentration, average smoke concentration, and average harmful gas concentration; the battery state characteristics include battery capacity, battery internal resistance and voltage variation; the vibration characteristics include vibration amplitude and vibration frequency;
the calculation formula of the average temperature Pw specifically includes:where wi represents the temperature value of the ith sample and n represents the total number of temperature samples collected;
the calculation formula of the temperature change rate Lw specifically comprises:wherein W2-W1 represents temperature values at two consecutive time points, Δt represents a time interval;
the calculation formula of the temperature gradient Tdw specifically comprises the following steps:wherein T is i+1 Representing the temperature value of the (i+1) th node, T i Representing the temperature value of the ith node, Δx representing the spacing between nodes; wherein the node refers to the position of the temperature sensor in space;
the calculation formula of the average humidity Ps specifically includes:where si represents the humidity value of the ith sample and m represents the total number of humidity samples collected;
the calculation formula of the humidity change rate Ls specifically includes:wherein S2-S1 represent humidity values at two consecutive time points of the battery pack, Δt represents a time interval;
the calculation formula of the average oxygen concentration Dy specifically comprises the following steps:where yi represents the oxygen concentration of the ith sample and q represents the total number of oxygen concentration samples collected;
the calculation formula of the average smoke concentration Dw specifically comprises the following steps:where ywi denotes the smoke concentration of the i-th sample, and a denotes the total number of smoke concentration samples collected;
the calculation formula of the average harmful gas concentration Dyh specifically comprises the following steps:wherein yhi represents the harmful gas concentration of the ith sample, and b represents the total number of collected harmful gas concentration samples;
the calculation formula of the battery capacity Cdr specifically comprises the following steps:where l represents the battery charge current and tc represents the battery charge time;
the calculation formula of the battery internal resistance Cdn specifically comprises the following steps:where V1 represents the voltage at which the discharge of the battery pack starts, V2 represents the voltage at which the discharge of the battery pack ends, and l2 represents the discharge current of the battery pack;
the calculation formula of the voltage change Cdb specifically comprises:where Va-Vb represents the voltage of the battery pack at two consecutive time points and Δt represents the time interval;
the calculation formula of the vibration frequency Zp specifically comprises:where pi represents the sampled value of the vibration signal and N represents the total number of sampled points;
the vibration frequency finds out a main frequency component by carrying out Fourier transform on the vibration signal; the fourier transform converts the vibration signal in the time domain into the frequency domain, and the frequency corresponding to the maximum peak is the main frequency component, which belongs to the prior art means, so the embodiment does not make a specific description;
a3, building a neural network model through a neural network according to a data set containing various abnormal type samples and the extracted characteristics, so that the model can learn the characteristics and modes of different abnormal types; the treatment process is as follows:
a31, dividing a data set containing various abnormal type samples into a training set and a testing set; the training set is used for training the neural network model, and the testing set is used for evaluating the performance of the model;
a32, designing a structure of the neural network, and determining that the number of nodes of an input layer of the neural network is 13 according to the extracted characteristics, wherein each node corresponds to one characteristic; determining the number of hidden layers in the neural network and the number of neurons of each hidden layer; for each neuron, selecting a Sigmoid activation function; training a neural network model by selecting a random gradient descent algorithm;
a33, training the neural network model by using a training set, wherein the processing procedure is as follows:
a331, initializing weight and bias according to the number of nodes of each layer in the network structure, randomly sampling the initialized weight from standard normal distribution, and initializing the bias to zero;
a332, inputting the characteristic data of the training set into a network, calculating the activation value of each neuron through the parameter of each layer, and executing the forward propagation process between the hidden layer and the output layer;
assuming that the input feature data is c, the weight of the first layer is r l Bias of the first layer is v l The activation function of the first layer is f l The neuron activation value of the o+1 layer is calculated by the following formula:
，/>wherein J o+1 Represents the weighted sum of the layer-o+1 neurons, M o+1 Represents the output value of the (o+1) -th layer neuron, M o Representing the output value of the layer o neuron, r o+1 Weight matrix representing layer o+1, v o+1 A bias term representing layer o+1;
a333, after forward propagation, comparing the predicted result ypr of the model with the actual tag ytr, calculating a Loss function Loss,wherein a represents the number of samples;
a334, using a back propagation algorithm to propagate the error signal from the output layer to the hidden layer, and calculating the gradient of each parameter by using a chain rule; the counter propagation algorithm and the chain method belong to the prior art means, so the embodiment does not make a specific description;
a335, updating the weight and the bias among layers according to the gradient obtained by calculation by adopting a random gradient descent algorithm; the random gradient descent algorithm belongs to the prior art means, so the embodiment does not make a specific description;
a336, repeatedly executing the steps A332 to A335 until the preset iteration times are reached or convergence conditions are met;
a4, evaluating the performance of the model by using a test set, wherein the performance comprises accuracy, precision and recall index; according to the evaluation result, the network architecture and the super parameters are adjusted to optimize the performance of the model;
a5, deploying the model which is subjected to training and tuning into practical application, and processing unknown data and identifying abnormal type data so as to judge the abnormal type; for example, whether the input data has abnormal type data or not is identified through the model, and if the abnormal temperature data exists, a temperature abnormal result is output;
a6, predicting the future development trend of the abnormal type data by adopting an autoregressive moving average model so as to make timely response and decision; the treatment process is as follows:
a61, arranging the abnormal type data according to a time sequence, and selecting each minute as a time interval to serve as a time unit of an autoregressive moving average model;
a62, determining the orders g and h of an autoregressive moving average model by observing an autocorrelation graph and a partial autocorrelation graph of the time sequence; wherein the order g represents the number of autoregressive items, the order h represents the number of moving average items, and the optimal g and h values are estimated according to the tail cutting conditions of the autocorrelation diagrams and the partial autocorrelation diagrams; wherein the autocorrelation graph shows the correlation between the time series and its delayed version, if the autocorrelation graph is truncated to zero rapidly after a certain delay point, meaning that an autoregressive model can be used, and the order g can be determined by the delay point of the last non-zero autocorrelation coefficient; the partial autocorrelation graph shows the correlation between two time sequences after eliminating the influence of other delay terms, if the partial autocorrelation graph is truncated to zero rapidly after a certain delay point, it means that a moving average model can be used, and the order h can be determined by the delay point of the last non-zero partial autocorrelation coefficient; comprehensively considering the tail cutting condition of the autocorrelation diagrams and the partial autocorrelation diagrams, wherein the autocorrelation diagrams are rapidly cut off to zero after a certain delay point, and the partial autocorrelation diagrams are also rapidly cut off to zero after the delay point, so that the optimal g and h values are obtained;
a63, fitting an autoregressive moving average model on historical data by using the selected g and h values; the parameters of the model are determined specifically using maximum likelihood estimation, which belongs to the prior art means, so the embodiment does not make a specific description;
a64, diagnosing the fitted autoregressive moving average model, checking whether the residual error accords with a white noise assumption, namely whether the residual error has randomness and stationarity, and evaluating the adaptability of the model by checking an autocorrelation diagram and a partial autocorrelation diagram of the residual error and performing an Ljung-Box test method; the diagnosis of the fitted autoregressive moving average model belongs to the prior art means, so the embodiment does not make a specific description;
a65, predicting the future abnormal type data by using the trained autoregressive moving average model; generating a future predicted value in a recursion mode according to the historical observed value and the model parameter, and obtaining an abnormal type data development trend predicted value; predicting the future abnormal type data by using a trained autoregressive moving average model to obtain a series of predicted values, wherein the predicted values represent the development trend of the abnormal type data at the future time point;
the system monitoring module comprises a parameter calculation unit and a fault alarm unit; the parameter calculation unit is used for calculating the health index of the energy storage PACK according to the preprocessed data of the energy storage PACK; the fault alarm unit is used for calculating an abnormal degree coefficient of the energy storage PACK according to the health degree index, the abnormal type data and the development trend predicted value of the abnormal type data of the energy storage PACK, dividing the abnormal alarm grade according to the abnormal degree coefficient and transmitting the abnormal alarm grade to the system control module; the method is beneficial to timely finding out abnormal conditions and adopting alarm grade division so as to ensure the classification of alarm information and realize quick early warning, thereby reducing the confusion of the alarm information;
the implementation needs to specifically explain that the specific processing procedure of the parameter calculation unit is as follows:
b1, calculating a charge/discharge ratio, energy efficiency, cycle efficiency, a charge time recovery coefficient and a self-discharge rate according to the data after the energy storage PACK pretreatment;
the calculation formula of the charge/discharge ratio Bcf is as follows:wherein cn represents charging energy, fn represents discharging energy, and the charging/discharging ratio reflects the charging/discharging balance of the energy storage PACK in the using process;
the calculation formula of the energy efficiency Xnl is as follows:where ynl represents useful energy extracted from the stored energy PACK, znl represents total energy input, and energy efficiency represents the loss of the stored energy PACK during energy conversion;
the calculation formula of the cycle efficiency Xxh is as follows:wherein scn represents the energy output in the ith charge and discharge cycle, srn represents the energy input in the ith charge and discharge cycle, H represents the number of charge and discharge cycles, and the cycle efficiency is used for evaluating the energy conversion efficiency of the energy storage PACK in the charge and discharge cycles;
the calculation formula of the charging time recovery coefficient Xcs is as follows:wherein Tc represents the time when the energy storage PACK is fully charged, tf represents the time when the energy storage PACK is discharged to 80% of the battery capacity, deltaSOC represents the charge loss of the energy storage PACK in a discharge state, and the charge time recovery coefficient is used for evaluating the charge speed and recovery capacity of the energy storage PACK;
the self-discharge rate Lzfd is calculated according to the following formula:where SOCt represents the state of charge at time t, SOC0 represents the state of charge at the initial point in time, Δtc represents the time difference between the two points in time, and the self-discharge rate is used to evaluate the charge retention capability of the stored energy PACK in the idle state;
b2, calculating the health index ZJK of the energy storage PACK according to the charge/discharge ratio Bcf, the energy efficiency Xnl, the cycle efficiency Xxh, the charge time recovery coefficient Xcs and the self-discharge rate Lzfd,
whereintaAndtbrespectively representing a start time and an end time defining a time range of the integration,α、β、γ、δ、εthe scale factor of each term is represented,kan adjustment factor indicating a self-discharge rate; the size of the proportionality coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the proportionality coefficient is only required to be about the size of the proportionality coefficient without influencing the proportionality relation between the parameter and the quantized numerical value;
it should be noted that, in the health index calculation formula of the energy storage PACK, the range of the charge/discharge ratio is mapped into a wider value range by taking logarithms, so that the influence degree is more balanced; by using a square root function, the change of the energy efficiency is converted from a linear relation to a nonlinear relation so as to better reflect the influence of the energy efficiency on the health degree; by multiplying the cycle efficiency by the difference of 1, the change in cycle efficiency can be translated into a positive health impact; the change of the charge time recovery coefficient can be converted into the forward health degree influence by taking the reciprocal; by applying an exponential function, the change of the self-discharge rate can be converted from a linear relationship to a nonlinear relationship, and the influence degree is gradually reduced;
the implementation needs to specifically explain that the specific processing procedure of the fault alarm unit is as follows:
c1, carrying out normalization processing according to a health index ZJK of the energy storage PACK, and converting the health index ZJK into a range from 0 to 1; the specific calculation formula of normalization is as follows:wherein ZJKmin represents the minimum value of the health index ZJK, ZJKmax represents the maximum value of the health index ZJK;
c2, giving weights of different grades according to the quantity and the severity of the abnormal type data, and marking the weight of the abnormal degree as ui;
it should be noted that, the giving of weights of different levels means that weights of different anomaly types of data are determined according to specific requirements and domain knowledge in practical application; for example, according to the specific situation of the energy storage PACK, the following factors are considered to make the weight of the abnormality degree:
types of exception type data: judging the influence degree of the abnormal type data on the safety and performance of the energy storage PACK according to parameters, sensor information and the like related to the abnormal type data, and giving higher or lower weight correspondingly;
quantity of anomaly type data: considering the occurrence times of the abnormal type data, more abnormal type data can have greater influence on the health degree of the energy storage PACK, so that higher weight can be given;
severity of anomaly type data: depending on the degree or level of anomaly type data, e.g. warning, error, different weights are given, higher severity anomaly type data may lead to greater security risk or performance loss and should therefore be given higher weights;
c3, calculating the abnormality degree coefficient U of the energy storage PACK according to the health degree index GZJK, the abnormality type data and the development trend predicted value of the abnormality type data of the energy storage PACK,wherein D represents the number of abnormal type data, each having a corresponding weight ui and a development trend predictive value Gi; the predicted value is a development trend predicted value corresponding to the current time point; since the predicted value of the trend of the anomaly data is predicted according to the historical observed value and the model parameter, the predicted value is usually a time-varying sequence, and each time point has a corresponding predicted value, so when calculating the anomaly degree coefficient U, the predicted value of the trend corresponding to the current time point needs to be used;
c4, judging and comparing the abnormality degree coefficient U of the energy storage PACK with a preset abnormality degree threshold U, if U is larger than the U threshold, marking the current abnormality type data as first-level alarm information and transmitting the first-level alarm information to a system control module, and if U is smaller than or equal to the U threshold, marking the current abnormality type data as second-level alarm information and transmitting the second-level alarm information to the system control module; the primary alarm information generally indicates that the energy storage PACK has serious abnormal conditions, and control measures need to be taken in time to prevent further development; the secondary alarm information generally indicates that the energy storage PACK has slight abnormality but has not reached a critical state; the preset abnormality degree threshold U threshold can be specifically set according to specific conditions, and specific data is not specifically limited in the embodiment;
the system control module is used for receiving the data transmitted by the system monitoring module, implementing a corresponding control strategy according to the primary alarm information or the secondary alarm information so as to adapt to different fire situations and requirements, helping the system to make more accurate judgment and decision and ensuring the safe operation of the energy storage PACK; the primary alarm information or the secondary alarm information comprises an abnormality type, abnormality type data and abnormality occurrence time;
the implementation needs to specifically explain that the specific processing mode of the system control module is as follows: if the alarm information is the primary alarm information, starting the fire extinguishing device according to the abnormal type and preset fire extinguishing device control logic to extinguish potential fire or thermal events; an alarm signal is sent to personnel in a sound and light flashing mode to remind the energy storage PACK of serious abnormality; isolating the energy storage PACK from the system, and cutting off the connection with other equipment to avoid further diffusion of abnormality; if the alarm information is the secondary information, according to the abnormal type data and the development trend predicted value of the abnormal type data, the operation parameters of the energy storage PACK, such as a temperature set value, a humidity control strategy, a gas concentration threshold value and the like, are adjusted so as to reduce the influence of the abnormality; arranging maintenance personnel to overhaul, maintain or replace related parts so as to repair abnormal conditions or prevent future abnormal occurrence; a prompt message is sent to related personnel through a display screen and a mobile phone application mode to remind the energy storage PACK of abnormality, and attention and processing are needed;
the system execution module is used for executing the instruction sent by the system control module and feeding back the execution result and the state information to the system control module.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. Energy storage fire control system based on thing networking, its characterized in that: comprising the following steps:
and a data acquisition module: the data preprocessing module is used for acquiring the original data of the energy storage PACK and transmitting the original data to the data preprocessing module for processing and analysis; the raw data comprises battery pack temperature, battery pack humidity, gas concentration in a battery box, battery pack electric quantity, battery pack charging current, battery pack discharging voltage, battery pack vibration and battery box pressure data;
and a data preprocessing module: the method comprises the steps of preprocessing collected original data, including noise removal, filtering and calibration operations;
and a data analysis module: the method comprises the steps of analyzing preprocessed data, identifying abnormal type data to judge the abnormal type by constructing a neural network model, and predicting the development trend of the abnormal type data;
the system monitoring module comprises a parameter calculation unit and a fault alarm unit; the parameter calculation unit is used for calculating the health index of the energy storage PACK according to the preprocessed data of the energy storage PACK; the fault alarm unit is used for calculating an abnormal degree coefficient of the energy storage PACK according to the health degree index, the abnormal type data and the development trend predicted value of the abnormal type data of the energy storage PACK, dividing the abnormal alarm grade according to the abnormal degree coefficient and transmitting the abnormal alarm grade to the system control module;
and a system control module: the system monitoring module is used for receiving data transmitted by the system monitoring module and implementing a corresponding control strategy according to the primary alarm information or the secondary alarm information;
the system execution module: the system control module is used for executing the instruction sent by the system control module and feeding back the execution result and the state information to the system control module;
the specific analysis process of the data analysis module is as follows:
a1, marking and classifying the preprocessed data according to different anomaly types to obtain anomaly type data; the abnormality type data includes temperature abnormality data, humidity abnormality data, gas concentration abnormality data, battery state abnormality data, and vibration abnormality data;
a2, extracting temperature characteristics, humidity characteristics, gas concentration characteristics, battery state characteristics and vibration characteristics from the abnormal type data; the temperature characteristics include average temperature, rate of temperature change, and temperature gradient; the humidity characteristics include average humidity and humidity rate of change; the gas concentration characteristics include average oxygen concentration, average smoke concentration, and average harmful gas concentration; the battery state characteristics include battery capacity, battery internal resistance and voltage variation; the vibration characteristics include vibration amplitude and vibration frequency;
a3, building a neural network model through a neural network according to a data set containing various abnormal type samples and the extracted characteristics;
a4, evaluating the performance of the model by using a test set, wherein the performance comprises accuracy, precision and recall index; according to the evaluation result, the network architecture and the super parameters are adjusted to optimize the performance of the model;
a5, deploying the model which is subjected to training and tuning into practical application, and processing unknown data and identifying abnormal type data so as to judge the abnormal type;
a6, predicting the future development trend of the abnormal type data by adopting an autoregressive moving average model.
2. The energy storage fire control system based on the internet of things according to claim 1, wherein: according to the data set containing various abnormal type samples and the extracted characteristics, a neural network model is established through a neural network, and the processing procedure is as follows:
a31, dividing a data set containing various abnormal type samples into a training set and a testing set;
a32, designing a structure of the neural network, and determining that the number of nodes of an input layer of the neural network is 13 according to the extracted characteristics, wherein each node corresponds to one characteristic; determining the number of hidden layers in the neural network and the number of neurons of each hidden layer; for each neuron, selecting a Sigmoid activation function; training a neural network model by selecting a random gradient descent algorithm;
a33, training the neural network model by using a training set, wherein the processing procedure is as follows:
a331, initializing weight and bias according to the number of nodes of each layer in the network structure, randomly sampling the initialized weight from standard normal distribution, and initializing the bias to zero;
a332, inputting the characteristic data of the training set into a network, calculating the activation value of each neuron through the parameter of each layer, and executing the forward propagation process between the hidden layer and the output layer;
a333, after forward propagation, comparing the predicted result ypr of the model with the actual tag ytr, calculating a Loss function Loss,wherein a represents the number of samples;
a334, using a back propagation algorithm to propagate the error signal from the output layer to the hidden layer, and calculating the gradient of each parameter by using a chain rule;
a335, updating the weight and the bias among layers according to the gradient obtained by calculation by adopting a random gradient descent algorithm;
a336, repeatedly executing the steps A332 to A335 until the preset iteration times are reached or the convergence condition is met.
3. The energy storage fire control system based on the internet of things according to claim 1, wherein: the method adopts an autoregressive moving average model to predict the future development trend of the abnormal type data, and comprises the following processing procedures:
a61, arranging the abnormal type data according to a time sequence, and selecting each minute as a time interval to serve as a time unit of an autoregressive moving average model;
a62, determining the orders g and h of an autoregressive moving average model by observing an autocorrelation graph and a partial autocorrelation graph of the time sequence; wherein the order g represents the number of autoregressive terms and the order h represents the number of moving average terms;
a63, fitting an autoregressive moving average model on historical data by using the selected g and h values;
a64, diagnosing the fitted autoregressive moving average model, and checking whether the residual error accords with a white noise assumption or not;
a65, predicting the future abnormal type data by using the trained autoregressive moving average model; and generating a future predicted value in a recursion mode according to the historical observed value and the model parameter, and obtaining an abnormal type data development trend predicted value.
4. The energy storage fire control system based on the internet of things according to claim 1, wherein: the specific processing procedure of the parameter calculation unit is as follows:
b1, calculating a charge/discharge ratio, energy efficiency, cycle efficiency, a charge time recovery coefficient and a self-discharge rate according to the data after the energy storage PACK pretreatment;
b2, calculating the health index ZJK of the energy storage PACK according to the charge/discharge ratio Bcf, the energy efficiency Xnl, the cycle efficiency Xxh, the charge time recovery coefficient Xcs and the self-discharge rate Lzfd,
whereintaAndtbrespectively represent the positionsThe start time and end time of the time range of the sense integral,α、β、γ、δ、εthe scale factor of each term is represented,kindicating the adjustment factor of the self-discharge rate.
5. The energy storage fire control system based on the internet of things according to claim 1, wherein: the specific processing procedure of the fault alarm unit is as follows:
c1, carrying out normalization processing according to a health index ZJK of the energy storage PACK, and converting the health index ZJK into a range from 0 to 1; the specific calculation formula of normalization is as follows:wherein ZJKmin represents the minimum value of the health index ZJK, ZJKmax represents the maximum value of the health index ZJK;
c2, giving weights of different grades according to the quantity and the severity of the abnormal type data, and marking the weight of the abnormal degree as ui;
c3, calculating the abnormality degree coefficient U of the energy storage PACK according to the health degree index GZJK, the abnormality type data and the development trend predicted value of the abnormality type data of the energy storage PACK,wherein D represents the number of abnormal type data, each having a corresponding weight ui and a development trend predictive value Gi;
and C4, judging and comparing the abnormality degree coefficient U of the energy storage PACK with a preset abnormality degree threshold U, if the U is larger than the U threshold, marking the current abnormality type data as first-level alarm information and transmitting the first-level alarm information to the system control module, and if the U is smaller than or equal to the U threshold, marking the current abnormality type data as second-level alarm information and transmitting the second-level alarm information to the system control module.
6. The energy storage fire control system based on the internet of things according to claim 1, wherein: the specific processing mode of the system control module is as follows: if the alarm information is the primary alarm information, starting the fire extinguishing device according to the abnormal type and preset fire extinguishing device control logic to extinguish potential fire or thermal events; an alarm signal is sent to personnel in a sound and light flashing mode to remind the energy storage PACK of serious abnormality; isolating the energy storage PACK from the system, and cutting off the connection with other equipment; if the alarm information is the secondary information, adjusting the operation parameters of the energy storage PACK according to the abnormal type data and the development trend predicted value of the abnormal type data; arranging maintenance personnel to overhaul, maintain or replace related parts; and a prompt message is sent to related personnel through a display screen and a mobile phone application mode to remind the energy storage PACK of abnormality, and attention and processing are needed.
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